Systems and methods for locating a guest in a facility for order delivery

ABSTRACT

A delivery system may include one or more processors and memory storing instructions executable by the one or more processors to cause the one or more processors to identify one or more attributes of a user in one or more images and associate the one or more attributes of the user with an order placed by the user. The delivery system may also track the one or more attributes of the user in the one or more images over time to identify movement of the user within an environment and in response to the one or more attributes of the user in the one or more images remaining at a location for more than a threshold time, create an association between the one or more attributes of the user and the location. The delivery system may then provide an instruction to deliver items in the order to the location.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.63/347,404, entitled “SYSTEMS AND METHODS FOR LOCATING A GUEST IN AFACILITY FOR ORDER DELIVERY,” filed May 31, 2022, the entire contents ofwhich is hereby incorporated by reference in its entirety for allpurposes.

BACKGROUND

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present techniques,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

In a restaurant or dining hall facility, a guest may place an order atan ordering station (e.g., kiosk, register) and be given a card with aprinted number. Then, at a later time, a server may search for the cardwith the printed number (e.g., visually observe the card with theprinted number on a table) to deliver the order to the guest. In somecases, the guest may be given an output device (e.g., a buzzer) or usesome other output device (e.g., mobile phone; wall-mounted electronicdisplay), and the output device is instructed to provide a notificationthat indicates that the order is ready at a pickup station. Then, theguest may travel to collect the order at the pickup station. In somecases, the guest may wait for their name to be called, and this is thenotification that indicates that the order is ready at the pickupstation.

SUMMARY

Certain embodiments commensurate in scope with the originally claimedsubject matter are summarized below. These embodiments are not intendedto limit the scope of the claimed subject matter, but rather theseembodiments are intended only to provide a brief summary of possibleforms of the subject matter. Indeed, the subject matter may encompass avariety of forms that may be similar to or different from theembodiments set forth below.

In an embodiment, a delivery system may include one or more processorsand memory storing instructions executable by the one or more processorsto cause the one or more processors to identify one or more attributesof a user in one or more images captured by one or more cameras andassociate the one or more attributes of the user with an order placed bythe user. The delivery system may also track the one or more attributesof the user in the one or more images over time to identify movement ofthe user within an environment and in response to the one or moreattributes of the user in the one or more images remaining at a locationfor more than a threshold time, create an association between the one ormore attributes of the user and the location. The delivery system maythen provide an instruction to deliver items in the order to thelocation.

In an embodiment, a method of operating a delivery system may includeidentifying, using one or more processors, one or more attributes of auser in one or more images captured by one or more cameras andassociating the one or more attributes of the user with an order placedby the user. The method may also track using the one or more processors,the one or more attributes of the user in the one or more images overtime to identify movement of the user within an environment and createan association between the one or more attributes of the user and thelocation in response to the one or more attributes of the user in theone or more images remaining at a location for more than a thresholdtime. The method may then provide an instruction to deliver items in theorder to the location.

In an embodiment, a delivery system may include one or more processorsand memory storing instructions executable by the one or more processorsto cause the one or more processors to identify one or more attributesof a user in one or more images captured by one or more cameras andassociate the one or more attributes of the user with an order placed bythe user. The processors may also track the one or more attributes ofthe user in the one or more images over time to identify movement of theuser within an environment and in response to the one or more attributesof the user in the one or more images remaining at a location for morethan a threshold time, create an association between the one or moreattributes of the user and the location. The processor may then providean output that indicates the location to facilitate delivery of items inthe order to the location without displaying the one or more images topersonnel associated with the environment.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic diagram of an embodiment of a delivery system thatmay be used a dining environment, in accordance with an aspect of thepresent disclosure;

FIG. 2 is a schematic illustration of a guest creating an order at apoint of sale terminal via the delivery system of FIG. 1 , in accordancewith an aspect of the present disclosure;

FIG. 3 is a schematic illustration one or more attributes of the guestthat may be identified by the delivery system of FIG. 1 , in accordancewith an aspect of the present disclosure;

FIG. 4 is a schematic illustration of the delivery system and the diningenvironment of FIG. 1 that shows an association of a location of thedining environment with the one or more attributes of the guest, inaccordance with an aspect of the present disclosure;

FIG. 5 is a schematic illustration of the delivery system and the diningenvironment of FIG. 1 that shows delivery of the order to the guest, inaccordance with an aspect of the present disclosure;

FIG. 6 is a flowchart of an embodiment of a process for delivering theorder to the location of the dining environment via the delivery systemof FIG. 1 , in accordance with an aspect of the present disclosure;

FIG. 7 is a schematic illustration of the delivery system and the diningenvironment of FIG. 1 that shows a group of guests , in accordance withan aspect of the present disclosure; and

FIG. 8 is a flowchart of an embodiment of a process for associating theone or more attributes of the guest with the location of the diningenvironment via the delivery system of FIG. 1 , in accordance with anaspect of the present disclosure.

DETAILED DESCRIPTION

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Additionally, it should be understood that references to “oneembodiment” or “an embodiment” of the present disclosure are notintended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

The present disclosure generally relates to systems and methods forreceiving and delivering an order in an environment, such as a diningenvironment. The dining environment may include a variety of features,such as vendors (e.g., restaurants, bakeries, ice cream stalls),merchants (e.g., retailers for clothing, accessories, and/or souvenirs),stations (e.g., drinks, condiments, utensils, hand sanitizer, trash),restrooms, and/or tables to provide a seamless and efficient diningexperience for a guest (e.g., customer). A delivery system may be usedto supplement or complement the features of the dining environment toreceive the order from the guest and to facilitate delivery of the orderto the guest. In an embodiment, the delivery system may facilitatedelivery of the order to the guest in a passive manner (e.g., passivefor the guest; the guest only needs to place the order and then walk toa table). For example, the delivery system may facilitate delivery ofthe order to the guest without providing a physical object (e.g., a cardwith a printed number, a buzzer, a radiofrequency tag or reader) to theguest upon receipt of the order and/or without a notification to theguest (e.g., calling a name, a sound output, a light output, a hapticoutput, a text message). Further, in an embodiment, the guest does notneed to have or use their mobile phone for location tracking and/orlinking to the order.

Advantageously, the guest may place the order with a vendor at a pointof sale terminal. The order may include one or more items (e.g., food,toys, beverages). After placing the order, the guest may move within thedining environment to select a location (e.g., table, seat) for a diningexperience. For example, the guest may sit down at a table for a meal,and the guest may wait for the one or more items in the order to bedelivered to the location. The delivery system may identify one or moreattributes of the guest (e.g., while the guest places the order) andassociate the one or more attributes with the order placed by the guest.The delivery system may receive image data of the dining environment andtrack the one or more attributes within the dining environment. Afterthe guest selects the location, such as sitting down at the table, thedelivery system may associate the one or more attributes of the guestwith the location, thereby associating the order with the location.After the order is completed (e.g., the one or more items in the orderis ready for delivery to the guest), the delivery system may provideinstructions to a server (e.g., personnel, employee) to deliver the oneor more items in the order to the location.

In an embodiment, the guest may select a location (e.g., table, seat),and then leave to visit a temporary location (e.g., restroom, station,merchant, hand sanitizing station). For example, the guest may sit downat the table, but then walk over to a vending machine to purchase adrink. The delivery system may associate the one or more attributes ofthe guest with the table, and may determine whether leaving the tablebreaks the association. The delivery system may consider any of avariety of factors to determine whether being at the table creates theassociation and/or whether leaving the table breaks the association. Thefactors may include a respective time at the table, a respective timeaway from the table, a type of the temporary location, movement orgestures made by the guest at the table, any items placed on the table,any other guests at the table, and the like. For example, the guest mayleave an object (e.g., water bottle) at the table to claim it as theirtable. As such, the delivery system may determine that the guest mayreturn to the table and maintain the association of the one or moreattributes of the guest with the table.

However, the guest may move from a first location (e.g., a first table)to a second location (e.g., a second table). For example, the guest maydetermine a first table to be too small. As such, the guest may chooseto move from the first table to a second table. The delivery system maydetermine that this movement of the guest from the first table to thesecond table is a break event. That is, the delivery system may breakthe association between the one or more attributes of the guest with thefirst table. In an embodiment, the delivery system may then associatethe one or more attributes of the guest with the second table.

In an embodiment, the delivery system may associate the one or moreattributes of the guest with the location in response to the guestspending a period of time at the location that meets or exceeds athreshold period of time (e.g., dwell time). For example, the guest maysit down at a table for a period of time that meets or exceeds thethreshold period of time, and then the delivery system may associate theone or more attributes of the guest with the table. The threshold periodof time may vary based on any of a variety of factors, such as movementor gestures made by the guest at the table, any items placed on thetable, any other guests at the table, a respective time spent at otherlocations visited between the point of sale and the location, and thelike. For example, the guest may sit down at the table, place their bagon the table, and start playing on their mobile phone, which mayindicate an intent to remain at the table during the dining experienceand may cause the delivery system to reduce threshold period of time(e.g., as compared to another guest who stands at the table, does notplace their bag on the table, and/or continues to look around the diningenvironment instead of playing on their mobile phone). In anotherexample, the guest may stand by the table while the guest waits forprevious occupants of the table to clear the table and leave. Becausethe guest is in a standing position, the delivery system may increasethe threshold period of time (e.g., as compared to another guest whosits at the table). As noted herein, when the order is complete, thedelivery system may provide a server with instructions to deliver theorder to the location.

In an embodiment, a group of guests may visit the dining environment.For example, a family unit may visit the dining environment for a familymeal. The family unit may visit the point of sale terminal of the vendorto place their order. A member of the family unit may place the orderfor all members of the family unit. The delivery system may identify oneor more attributes for at least one member of the family unit (e.g., themember who placed the order). In another example, the group of guestsmay visit the dining environment and separately place orders. Eachmember of the group may individually place their order at the point ofsale terminal of the vendor. That is, members of the group may visitdifferent vendors and/or place different orders at different terminalsof a same vendor. Although the group may visit separate point of saleterminals, the group may regroup or reconvene at a same location (e.g.,one table) within the dining environment. The delivery system may trackthe one or more attributes for each member as they travel within thedining environment. For example, one member (e.g., the same or differentfrom the member who placed the order) may claim a table for the group,while other members of the group may visit a condiment station, autensil station, a restroom, another vendor, or the like. The deliverysystem may determine that the member at the table may be claiming thetable for the group, as such the delivery system may associate the orderwith the table. Further, the delivery system may associate all of theorders of the group with the member at the table. As such, the deliverysystem may provide instructions to deliver all orders of the group tothe member at the table.

Embodiments of the present disclosure are directed to a delivery systemthat utilizes computer vision techniques to associate one or moreattributes of a guest with an order made by the guest. Then, thedelivery system utilizes the computer vision techniques to identify alocation of the one or more attributes within a dining environment,which then enables the delivery system to associate the order to thelocation. The delivery system may track movement of the guest within thedining environment based on the one or more attributes. The attributesof the guest may be anonymous attributes, such as a hair color, aclothing color, a clothing item, a gait, a personal item, an accessory,or the like. That is, the attributes may not include personallyidentifiable information (PII). The term PII may include informationthat directly identifies an individual (e.g., name, address, socialsecurity number, telephone number) or data elements regarding theindividual (e.g., a combination of gender, race, birth date, geographicindicator). As described herein, the delivery system may identify one ormore attributes of the guest following a completed order, associate theone or more attributes with the order, track the one or more attributeswithin the dining environment to associate a location with the one ormore attributes, and provide instructions to deliver the order to thelocation. In other words, the delivery system may associate the orderwith the location of the guest within the dining environment.Accordingly, the delivery system may facilitate delivery of orderswithout certain types of visual indicators that are provided to theguest within the dining environment for tracking purposes (e.g., cardswith printed numbers, which may be reused by multiple guests over time)and/or without notifications to the guest.

With the preceding in mind, FIG. 1 is a schematic diagram of anembodiment of a delivery system 10 that may be used in a diningenvironment 50, such as a food hall, a food court, a dining hall, a foodtruck park, an amusement park, or the like. The dining environment 50may include an open space, such as a walkable area (e.g., a queue orline) where guests may visit a vendor(s) 52, create the order at thepoint of sale terminal(s) 54 of the vendor(s) 52, select a table(s) 58,visit a station(s) 60, or otherwise navigate through the diningenvironment 50. The dining environment 50 may include an entrance andexit for the guests to enter or leave the premises. The vendor(s) 52 mayinclude a restaurant, a food truck, a dessert shop (e.g., bakery, icecream shop), a beverage shop (e.g., juice shop), or the like. Thevendor(s) 52 may include the point of sale terminal(s) 54 that mayreceive orders from the guests. Each point of sale terminal 54 may be akiosk and/or a mobile device, such as a tablet. It should be appreciatedthat at least one of the point of sale terminals 54 may include a mobiledevice, such as a mobile phone, that is owned/carried by one of theguests and that uses an application to interact with the control system64 or other vendor system to place the order. Each point of saleterminal 54 may display a menu of the vendor 52 and allow the guests tocomplete transactions (e.g., place an order) with the vendor 52. Thepoint of sale terminal(s) 54 may also be operated by and/or include aserver (e.g., human server) who may take the orders from the guests andcreate the orders with the mobile device.

The dining environment 50 may also include a guest area 56 where variousguests may be located. The guest area 56 may include the table(s) 58that guests may sit at or stand next to during their dining experience.For example, the guests may sit at the table(s) 58 to eat a meal. Thetable(s) 58 may include or be associated with one or more chairs, whichmay be movable chairs (e.g., not secured to a ground or to the table(s)58) and/or stationary chairs (e.g., bolted or fastened to a ground or tothe table(s) 58; picnic benches, metal chairs, wooden chairs). Thestation(s) 60 may be a temporary location(s) that the guests visit,typically before or after selecting the table(s) 58. The station(s) 60may include a restroom, a merchant or retailer, a hand sanitizingstation, a condiment station, a utensil station, a trash station, adrink fountain, or the like. For example, the guests may visit thecondiment station to get ketchup, barbeque sauce, salt, pepper, or thelike. The guests may also visit the restroom before sitting at thetable(s) 58. After the dining experience, the guests may clear theirtable and bring any trash to the trash station before leaving the diningenvironment 50.

In certain embodiments, the dining environment 50 may include one ormore cameras 62 that generate image data (e.g., moving image data, suchas video data) of the dining environment 50. The one or more cameras 62may transmit the image data to a control system 64 (e.g., electroniccontrol system) for processing (e.g., image analysis, machine learning,artificial intelligence, computer vision). The one or more cameras 62and the control system 64 may form the delivery system 10. In operation,the delivery system 10 generates and processes the image data of thedining environment 50 to identify one or more attributes of the guests.Further, the delivery system 10 may be trained with machine learningalgorithms or artificial intelligence to understand and/or makepredictions related to guest locations in the dining environment 50(e.g., whether to establish an association between an order and alocation and/or whether to break the association between the order andthe location). For example, the delivery system 10 may be trained withhistorical and/or modeled data representative of the dining environment50 to understand patterns of human behavior for selecting a locationand/or for leaving the location.

The control system 64 may include a memory 66 and one or more processors68 (e.g., processing circuitry). The memory 66 may include volatilememory, such as random-access memory (RAM), and/or non-volatile memory,such as read-only memory (ROM), optical drives, hard disc drives,solid-state drives, or any other non-transitory computer-readable mediumthat includes instructions to operate the delivery system. The memory 66may also include a database of attributes (e.g., characteristics, suchas identifiable objects, movements, gestures, clothing colors, gait,head shape; threshold time periods), a map (e.g., facility map of thedining environment 50), patterns of human behavior, historical data,machine learning algorithms, and/or other types of information for thecontrol system 64. The processing circuitry 68 may be configured toexecute the instructions. For example, the processing circuitry 68 mayinclude one or more application specific integrated circuits (ASICs),one or more field programmable gate arrays (FPGAs), one or more generalpurpose processors, or any combination thereof.

The delivery system 10 may generate image data (by cameras 62) andidentify an order placed by a guest at the point of sale terminal 54.The delivery system 10 may receive indication of the order based on theimage data and/or based on information provided to the delivery system10 by the point of sale terminal 54 (e.g., the control system 64 iscommunicatively coupled to the one or more cameras 62 and/or the pointof sale terminal 54 via a wireless or wired network). For example, thedelivery system 10 may process the image data to determine that theguest is interacting with the point of sale terminal 54 (e.g., the guestis scrolling through a menu at the point of sale terminal 54) and/orthat the guest has placed the order (e.g., an order number/identifier isdisplayed on a screen of the point of sale terminal 54 and captured inthe image data). That is, after entering payment information, the pointof sale terminal 54 may display text on the screen notifying the guestof the completed transaction. For example, the point of sale terminalmay display, ‘THANK YOU FOR ORDERING,’ or ‘ORDER NUMBER 123 ISCONFIRMED,’ or the like. The delivery system 10 may identify this screenin the image data and identify the order. Additionally or alternatively,the delivery system 10 may receive information from the point of saleterminal 54, such as information that the guest has selected one or moreitems from the menu and/or that the guest has entered paymentinformation to place the order (e.g., the order number/identifier iscommunicated to the control system 64). The delivery system 10 mayassociate a number or other identifier with the order. For example, theorder may be ‘Order No. 123.’ In an embodiment, the delivery system 10may also associate a time and/or a location of the point of saleterminal 54 of order creation.

While the guest is placing and/or upon placement of the order, thedelivery system 10 may identify one or more attributes of the guest andassociate the one or more attributes of the guest with the order. Asdescribed further in FIG. 3 , the one or more attributes may beappearance indicators and may not include personally identifiableinformation (PII). For example, the delivery system 10 may identify ahair color of the guest, a relative height or size of the guest, a headshape of the guest, a gait of the guest, a clothing color of the guest,and/or an object (e.g., personal possession) of the guest. The one ormore attributes may be sufficient to enable differentiation betweenmultiple different guests for tracking purposes within the diningenvironment 50 but may not indicate or include PII.

As further described in FIGS. 4 and 7 , the delivery system 10 mayimplement machine learning or computer vision techniques to understandpatterns of human behavior and associate the one or more attributes ofthe guest with a location within the dining environment 50. In this way,the delivery system 10 may associate the order with the location of theguest. For example, the guest may complete the order at the point ofsale terminal 54, select the table 58, and wait for their order. Thedelivery system 10 may associate the one or more attributes of the guestwith the table 58 and provide instructions to deliver the order to thetable 58. In another example, the guest may select the table 58, leavean object, then go to the station 60. The delivery system 10 mayassociate the guest with the table 58, even though the guest left. Thedelivery system 10 may identify the object of the guest 80 on the table58 and determine that the guest may return.

In an embodiment, the delivery system 10 may be configured to provideinstructions (e.g., audible instruction via a speaker and/or visibleinstructions via a display) to the vendor(s) 52 to facilitate deliveryof items of the order to the guest. For example, after ordering, theguest may travel to the guest area 56 and sit at the table 58. Thedelivery system (via the control system 64) may store or have access toa map of the dining environment 50. The map may associate objects withinthe dining environment 50 with a respective identifier, such as aletter, number, or a shape. For example, the tables 58 may be labeledA-F, respectively. In another example, the tables 58 may be labeled witha letter and a number, such as A1, A2, A3, B1, B2, and B3, respectively.Further, a seat of the tables 58 may be assigned a letter, number, orboth. For example, an instruction may be to provide Order 123 to TableA1, Seat 3, or provide Order 98 to Table A1, Seat J. Accordingly, thedelivery system 10 may facilitate delivery of orders using the imagedata and/or the map.

To ensure accurate instructions, the delivery system 10 may passivelyupdate the map of the dining environment 50. For example, the one ormore cameras 62 may continuously generate image data of the diningenvironment 50 while tracking the one or more attributes of the guest.The delivery system 10 may also identify a configuration or orientationof the tables 58 and/or a status of the tables 58 to update the map ofthe dining environment 50. For example, the guest may push one or moretables 58 together. The delivery system 10 may identify the combinedtables in the image data and update the map, including the respectiveidentifiers for the objects within the dining environment 50. In anotherexample, the delivery system 10 may identify one or more unavailabletables from the image data (e.g., waiting for dishes to be cleared) andunderstand that future guests may not want to sit at the one or moreunavailable tables. In an instance, the delivery system 10 may output anotification to the vendor 52 of the unavailable tables. In this way,the delivery system 10 may have a real-time or near real-timeunderstanding of the dining environment 50 and provide accurateinstructions for order delivery.

It should be appreciated that the layout and arrangement of the diningenvironment in FIG. 1 is merely exemplary, and the delivery system 10may be used with any of a variety of dining environments that arearranged in any suitable manner. Moreover, certain components of thedelivery system 10 may be shared between the vendors 52 and/orrespective components of the delivery system 10 may be provided for eachvendor (e.g., one or more cameras 62 for one vendor and one or morecameras 62 for another vendor). Indeed, the delivery system 10 may beshared between/in communication with multiple dining environments 50 ormay be dedicated to its own dining environment 50.

With the foregoing in mind, FIG. 2 is an example illustration of a guest80 creating and placing an order 82 at the point of sale terminal 54 ofthe vendor 52. For example, the guest 80 may view a menu or a list ofitems (e.g., goods, such as food, toys) that the vendor 52 sells at thepoint of sale terminal 54. The guest 80 may also select one or moreitems for purchase to create the order 82. The point of sale terminal 54may be a kiosk, a self-checkout station, a mobile device of the guest80, or the like. For example, the kiosk may include a display screenthat displays a menu provided of the vendor 52. The guest 80 may scrollthrough the menu and select one or more items for the order 82. Tofinalize the order 82, the guest 80 may enter a payment (e.g., creditcard information, cash, electronic transfer). For example, the guest 80may swipe the credit card or the debit card through a card reader on thekiosk or the guest 80 may insert cash into the kiosk. The kiosk may thendisplay a confirmation message, such as ‘ORDER COMPLETED,’ ‘THANK YOUFOR YOUR ORDER,’ ‘ORDER NUMBER 123 CONFIRMED’ or the like. As describedherein, the delivery system 10 may analyze image data (from the one ormore cameras 62) and identify the confirmation message.

With the foregoing in mind, the dining environment 50 may include twoguests (e.g., a first guest 80 a, a second guest 80 b). It may bebeneficial for the control system 64 to distinguish between the guests80 a, 80 b to accurately deliver orders. For example, the guests 80 a,80 b may visit the vendor 52 and/or the point of sale terminals 54. Forexample, the first guest 80 a may visit a first point of sale terminal54 a to create an first order 82 a. Upon confirmation of the first order82 a, the delivery system 10 may identify one or more attributes of thefirst guest 80 a and associate the attributes with the first point ofsale terminal 54 a and the first order 82 a. Similarly, the deliverysystem 10 may associate one or more attributes of the second guest 80 bwith a second point of sale terminal 54 b and/or a second order 82 b.The delivery system 10 may track the one or more attributes of the firstguest 80 a and the second guest 80 b within the dining environment 50for order delivery.

In an embodiment, the delivery system 10 may not associate the firstguest 80 a and the second guest 80 b as members of a group. For example,the delivery system 10 may recognize that the first guest 80 a arrivedbefore the second guest 80 b. In another example, the delivery system 10may identify the first guest 80 a as already a member of a group (e.g.,that does not include the second guest 80 b). In an embodiment, thedelivery system 10 may associate the first guest 80 a and the secondguest 80 b as members of the same group. For example, the first guest 80a and the second guest 80 b may arrive together at the diningenvironment 50 and/or interact with one another in the diningenvironment 50 (e.g., prior to placing the orders 82 a, 82 b). The firstguest 80 a and the second guest 80 b may choose to create their ownorders at the point of sale terminals 54. The delivery system 10 mayalso associate the first order 82 a placed by the first guest 80 a withthe second order 82 b placed by the second guest 80 b. In this way, thedelivery system 10 may have additional data points for determining alocation of the group (e.g., to the deliver the orders 82 a, 82 b). Asfurther described with reference to FIG. 4 , the first guest 80 a andthe second guest 80 b may take different routes (e.g., paths) to get toa location.

FIG. 3 is an example illustration of one or more attributes of the guest80 that may be identified and/or used by the delivery system 10 of FIG.1 for tracking the guest 80. The one or more attributes may be anonymousattributes or appearance indicators, rather than PII. For example, thedelivery system 10 may identify a hair color or hairstyle 90 a, a headshape 90 b, a clothing item shape and/or color 90 c, an accessory 90 d,an object (e.g., personal possession) 90 e, a gait 90 f, or the like.

For example, the delivery system 10 may identify the hair color 90 a ofthe guest 80. The hair color 90 a may include black, gray, white, brown,blonde, red, or a combination thereof. The hairstyle 90 a may include abraid, a ponytail, bangs, bald/lack of hair, or the like. The head shape90 b may be a shape of the guest's face, such as heart shaped, squareshaped, oval, diamond, triangle, or the like. The head shape 90 b mayalso include head or facial accessories, such as glasses, hats,piercings, or the like. In an embodiment, the delivery system 10 may usea combination of hair color and/or hairstyle 90 a and head shape 90 b asthe one or more attributes of the guest 80. Additionally oralternatively, the delivery system 10 may identify the clothing color 90c of the guest 80. For example, the delivery system 10 may identify acolor of the clothing, a pattern of the clothing, a design on theclothing, or the like. For example, the guest 80 may wear a shirt with aslogan, a cartoon character, or a graphic design.

In an embodiment, the delivery system 10 may identify one or moreaccessories 90 d of the guest 80. For example, the one or moreaccessories 90 d may include earrings, a necklace, a bracelet, a ring, ascarf, a hair clip, a tie clip, a belt, sunglasses, and/or otheraccessory worn by the guest 80. In the illustrated embodiment, thedelivery system 10 may identify a necklace with a charm. Additionally oralternatively, the delivery system 10 may identify one or more personalpossessions 90 e of the guest 80. The one or more personal possessions90 e may include a purse, a phone, a wallet, a jacket, a backpack, awater bottle, and/or other object carried by the guest 80. The one ormore personal possessions 90 e may also include a baby stroller, a pairof crutches, a wheelchair, and/or other object transported with theguest 80. For example, the delivery system 10 may identify a waterbottle as the personal possession 90 e. As described herein, thedelivery system 10 may track the personal possession 90 e within thedining environment 50 to determine the location of the guest 80.

In an embodiment, the delivery system 10 may track the gait 90 f of theguest 80. For example, the delivery system 10 may recognize,distinguish, and associate a walking gait 90 f with the guest 80. Forexample, the guest 80 may walk faster or slower relative to an averagewalking speed. In another example, the guest 80 may take larger orsmaller steps relative to an average step length. In another example,some guests 80 may have a unique walking style, such as skipping,bouncing, running, limping, or the like. The delivery system 10 may betrained (e.g., by artificial intelligence or machine learning) toidentify the walking gait 90 f with the guest 80. The one or moreattributes 90 may also include a size (e.g., estimated absolute sizeand/or relative size) of the guest 80. For example, the guest may be achild or a teenager that may be smaller relative to other guests. Inanother example, the guest may be a basketball player and taller thanother guests.

The delivery system 10 may track multiple attributes of the guest 80within the dining environment 50. For example, the delivery system 10may track the size and the walking gait 90 f of the guest 80. In aninstance, a taller guest may have a longer stride than a smaller guest.In another example, the delivery system 10 may track the hair color 90a, the accessories 90 d, and the personal possession 90 e. The deliverysystem 10 may track any number and/or combination of attributes 90 ofthe guest 80. The delivery system 10 may also track different attributes90 for different guests to thereby efficiently track the attributes 90that best differentiate the guests.

With the foregoing in mind, FIG. 4 is an example illustration of thedelivery system 10 tracking the first guest 80 a and the second guest 80b within the dining environment 50. For example, the first guest 80 amay walk to the guest area 56, select a table 58 a, visit a station 60,and then return to the table 58 a to wait for their order. As describedherein, the delivery system 10 may identify and track the one or moreattributes 90 of the guests 80 a, 80 b within the dining environment 50to provide instructions for order delivery.

For example, the first guest 80 a may travel within the diningenvironment 50 along a route 100. The first guest 80 a may enter theguest area 56 and visit a first table 58 a, as represented by point 102.The first guest 80 a may spend a period of time at the first table 58 ato establish ownership of the first table 58 a. The delivery system 10may compare the period of time at the first table 58 a to a thresholdperiod of time. In response to the period of time at the first table 58a meeting or exceeding the threshold period of time, the delivery system10 may associate the one or more attributes of the first guest 80 a withthe first table 58 a. Thus, the delivery system 10 may also associatethe order 82 a with the first table 58 a. In other words, the deliverysystem 10 may determine that the first table 58 a is the location fordelivery of the order 82 a. The first guest 80 a may leave the firsttable 58 a and visit the station 60, as represented by point 104. Thedelivery system 10 may identify the station 60 as a temporary location(e.g., as stored or labeled in a database accessible by the deliverysystem 10). In another example, the first guest 80 a may leave apersonal possession 90 e at the first table 58 a to claim the table. Thedelivery system 10 may identify the personal possession 90 on the firsttable 58 a as a claim to the table, and the delivery system 10 mayassociate the one or more attributes 90 of the first guest 80 a with thefirst table 58 a. The delivery system 10 may not break the associationof the one or more attributes 90 of the first guest 80 a, or the order82 a of the first guest 80 a, with the first table 58 a. Then, asrepresented by point 106, the first guest 80 a may return to the table58 a.

At the same time or another time, the second guest 80 b may go directlyto a second table 58 b along route 108. As represented by point 110, thesecond guest 80 b may take a seat at the second table 58 b and startbrowsing a mobile device, talking to other guests at the second table 58b, and/or taking some other action/gesture/movement that indicates anassociation with the second table 58 b. The delivery system 10 mayidentify one or more attributes 90 of the second guest 80 b. After thesecond guest 80 b sits at the second table 58 b, the delivery system 10may associate the one or more attributes 90 of the second guest 80 bwith the second table 58 b. The delivery system 10 may continuouslymonitor the image data to determine if a break event occurs, such as ifthe first guest 80 a and/or the second guest 80 b switches tables,leaves the dining environment 50, or the like. If the break event doesnot occur, the delivery system 10 may provide instructions to deliverthe orders 82 a, 82 b to the first and second tables 58 a, 58 b,respectively.

FIG. 5 is an example illustration of the delivery system 10 providinginstructions to facilitate delivery of the order 82 to the location ofthe guest 80. For example, the guest 80 may place the order 82 at thepoint of sale terminal 54 of the vendor 52 and the delivery system 10may identify one or more attributes of the guest 80. The vendor 52 mayreceive the order 82 and create the order 82. After the vendor 52prepares the order 82, the vendor 52 may indicate completion of theorder 82 to the delivery system 10. The delivery system 10 may provideinstructions to the vendor 52 indicative of the location for delivery ofthe order 82 (e.g., based on a current association of the location withthe order 82, via tracking the one or more attributes 90 of the guest 80who placed the order 82).

For example, the guest 80 may use the point of sale terminal 54 to placean order 82 for a kid's meal, including chicken nuggets and a toy. Thevendor 52 may create the order 82 in a kitchen. After the order 82 iscompleted (e.g., upon receipt of an indication, such as a user input,that the order 82 is completed), the delivery system 10 may provideinstructions to deliver the order 82 to the location of the guest 80.However, it should also be appreciated that the delivery system 10 mayprovide the instructions to deliver the order 82 to the location of theguest 80 in response to (e.g., as soon as) the order 82 being associatedwith the location (e.g., the guest 80 sits at the location in a mannerthat causes the delivery system 10 to associate the order 82 with thelocation). In an embodiment, all items of the order 82 may be madebefore the order 82 is delivered to the table 58. In other embodiments,each item of the order 82 is delivered as it is made to ensurefreshness.

The delivery system 10 may provide the location of the guest 80 to aserver of the vendor 52. The server may be a person or an automateddelivery system (e.g., remotely controlled or autonomously controlleddelivery vehicle). In an embodiment, the person delivering the food maybe a staff member of the vendor 52. The person may receive instructionsindicative of a table number/identifier, a seat number/identifier, or acombination thereof, within the dining environment 50. For example, theinstructions may include a string of text such as, ‘TABLE 1, SEAT 3’indicating a farthest left table and a farthest right seat. The deliverysystem 10 may output (e.g., display, via a device at the vendor 52and/or carried by the server) a map of the dining environment 50 withthe location labeled or otherwise highlighted on the map. As such, theserver may use the map to travel to the location. The map of the diningenvironment 50 may be updated in real-time (e.g., substantiallyreal-time, near-time) based on the image data. Further, the map of thedining environment 50 may include a schematic diagram or an image (e.g.,still or moving image) based on the image data. In an embodiment, theone or more attributes 90 of the guest 80 are not disclosed to theserver (e.g., hidden from the server; not known by the server), theguest 80 does not have any trackable items provided by the vendor 52(e.g., no card with a printed number, no buzzer, no RFID tag), and/orthe guest 80 does not use their own mobile device to provide locationdata to the delivery system 10 for delivery of the order 82. Instead,the server may deliver the items in the order 82 to the location basedon (e.g., only on) the map and/or the identifier(s) for the location. Inthis way, the server may not know any attributes of the guest 80 and maynot be prompted to visually confirm any attributes of the guest 80 priorto delivery of the order 82 to the location, rather the server may knowthe location designated for delivery of the order 82 placed by the guest80. Indeed, the guest 80 does not necessarily need to be present inorder for the server to complete the delivery of the order 82 placed bythe guest 80 to the location. The computer vision techniques and/oralgorithms may be sufficiently accurate and reliable (e.g., via machinelearning) to deliver the order 82 without these additional steps oradditional burdens on the server (e.g., without the server needing toknow and/or visually confirm the attributes of the guest 80).Accordingly, the server may bring the order 82 to the location and thenefficiently proceed to handle the orders of other guests.

In an embodiment, the server delivering the order 82 may be include theautomated delivery system, such as one or more robots, ground vehicles,aerial vehicles/drones, or any combination thereof. As such, thedelivery system 10 may transmit a signal indicative of the location tothe automated delivery system to facilitate delivery of the order 82 tothe location. The automated delivery system may receive and/or store themap of the dining environment 50, and the delivery system 10 may providea route from the vendor 52 to the location. The automated deliverysystem may include one or more sensors (e.g., motion sensors) toidentify objects (e.g., people, carts, animals) within the diningenvironment 50. In an embodiment, the automated delivery system may beprogrammed with machine learning, artificial intelligence, or computervision capabilities to interpret and understand the dining environment50. In this way, the delivery system 10 may provide instructions toprovide the order 82 to the location associated with the one or moreattributes 90 of the guest 80.

FIG. 6 is an example method 120 for identifying one or more attributes90 of the guest 80, associating the one or more attributes 90 of theguest 80 with a location, and delivering the order 82 to the locationwithin the dining environment 50. At block 122, the delivery system 10may receive an input indicative of an order 82 being placed by the guest80. The delivery system 10 may be communicatively coupled to the pointof sale terminal 54. That is, after the guest 80 completes the order 82,the point of sale terminal 54 may transmit a signal to the deliverysystem 10 indicative of the completed order 82. The delivery system 10may receive a receipt of the order 82, a confirmation of the order 82,or the like. The delivery system 10 may also identify the point of saleterminal 54 used by the guest 80 to create the order 82. The deliverysystem 10 may also assign a number or other identifier to the order 82.For example, the order number/identifier may be any combination ofnumbers or letters, such as 123 or A2.

In another example, the delivery system 10 may receive image data fromthe one or more cameras 62. The delivery system 10 may process the imagedata to receive the indication of the order 82 being placed by theguest. For example, the delivery system 10 may process the image dataand identify an order confirmation on the display of the point of saleterminal 54 and/or identify the guest 80 holding a payment method. Thedisplay of the point of sale terminal 54 may read, “THANK YOU FORORDERING,” or “ORDER NUMBER 123 CONFIRMED.” The delivery system 10 mayextract the order number or other identifier for the order 82 from theimage data (e.g., via text processing). In another example, the deliverysystem 10 may identify the guest 80 at the point of sale terminal 54scrolling through the menu and adding one or more items to a cart. Thedelivery system 10 may understand that adding items to the cart may leadto the guest 80 checking out and placing the order 82. As such, thedelivery system 10 may analyze the image data over time to receive theinput indicative of the order 82 being placed. For example, the deliverysystem 10 may identify image data indicative of the guest 80 interactingwith the point of sale terminal 54 or reaching for a wallet to completethe order 82.

In an embodiment, the delivery system 10 may identify the guest 80 atthe point of sale terminal 54, but may not receive the input indicativeof the order 82. For example, the guest 80 may scroll through the menuon a display of the point of sale terminal 54. However, the guest 80 maywalk away from the point of sale terminal 54 without completing theorder 82. In another example, the guest 80 may look at the point of saleterminal 54 and walk away without placing the order 82. As such, thedelivery system 10 may not isolate or identify attributes of the guest80 in the image data and may continue processing image data for ordersplaced by the guests 80.

At block 124, the delivery system 10 may generate and process image dataof the dining environment 50 and identify the guest 80. For example, theone or more cameras 62 may generate image data and the control system 64may process the image data to identify and/or to isolate image data ofthe guest 80. In an instance, in response to the guest 80 interactingwith the point of sale system 54, the delivery system 10 may identifyand isolate the image data of the guest 80.

At block 126, the delivery system 10 may identify one or more attributes90 of the guest 80 based on the isolated image data. As described withreference to FIG. 3 , the attributes 90 may be anonymous, appearanceindicators. The delivery system 10 may identify one or more attributes90 (e.g., a combination of attributes that sufficiently distinguish theguest 80 from other guests) and use the one or more attributes 90 totrack the guest 80 within the dining environment 50. For example, theguest 80 may be carrying a backpack, have a fast gait, and wear glasses.Further, another guest 80 may have blue hair, a cast on a leg, and/oruse crutches. The delivery system 10 may identify the height or size ofthe guest 80 to be smaller than an average size. The delivery system 10may estimate the height or size of the guest 80 based on characteristicsof the camera 62 (e.g., field of view, location relative to the diningenvironment 50) and/or characteristics of the dining environment 50(e.g., a size of the tables 58 within the dining environment 50). Inthis way, the delivery system 10 may track the guest 80 and other gueststravelling within the dining environment 50.

It should be appreciated that the delivery system 10 may receive andanalyze the image data while the order 82 is being placed by the guest80. For example, the delivery system 10 may begin to identify the one ormore attributes 90 of the guest 80 upon the guest 80 initiatinginteraction with the point of sale terminal 54 or otherwise taking stepsthat typically lead to the order 82 being placed. In one embodiment, thedelivery system 10 may identify image data indicative of the guest 80interacting with the point of sale terminal 54 or reaching for a walletto complete the order 82, and the delivery system 10 may then beginidentifying and creating a profile of the one or more attributes 90 ofthe guest 80.

At block 128, the delivery system 10 may associate the one or moreattributes of the guest 80 with the order 82. For example, the deliverysystem 10 may associate the one or more attributes 90, such as thebackpack, fast gait, and glasses of the guest 80 with the order 82.

At block 130, the delivery system 10 may monitor the image data overtime to associate the one or more attributes 90 of the guest 80 with alocation. As described with reference to FIGS. 4 and 7 , the guest 80may take any of a variety of routes before selecting a table 58. Assuch, it may be beneficial for the delivery system 10 to continuouslymonitor image data to identify the location (e.g., the location fordelivery of the order 82) of the guest 80 within the dining environment50. For example, the guest 80 may first select and sit down at the table58, then leave the table 58 to visit the station 60. The guest 80 mayleave their backpack at the table 58 so that other guests may understandthat the table 58 is claimed. Additionally, the delivery system 10 mayidentify the backpack on the table 58 as an indicator that the guest 80intends to return to the table 58. As such, the delivery system 10 mayassociate the one or more attributes 90 of the guest 80 with the table58.

At block 132, the delivery system 10 may provide instructions to deliverthe order 82 to the location. That is, the delivery system 10 mayprovide instructions to a server of the vendor 52 to deliver the order82 to the location. For example, the delivery system 10 may provide anotification to the server to bring the order 82 to the table 58.Accordingly, the delivery system 10 may receive the order 82 from theguest 80, track the guest 80 to determine the location for orderdelivery, and provide instructions for delivering the order 82. Itshould be appreciated that the method 120 may be carried out formultiple guests at the same time, at overlapping times, and/or atdifferent times (e.g., as the multiple guests enter the diningenvironment 50 and place respective orders over time).

The method 120 may be carried out according to instructions stored onone or more tangible, non-transitory, machine-readable media and/or maybe performed by the processor or the processing circuitry of thedelivery system 10 (via the control system 64) described herein or onanother suitable controller. The blocks of the method 120 may beperformed in any suitable order. Furthermore, certain blocks of themethod 120 may be omitted and/or other blocks may be added to the method120.

In an embodiment, a group of guests may visit the dining environment 50for the dining experience. The group may stay together and place oneorder 82 or the group may separate and place multiple orders 82 atmultiple point of sale terminals 54 and/or at multiple vendors 52. Afterplacing the order(s) 82, the group may head to the guest area 56 to waitfor the order(s) 82. FIG. 7 is an example illustration of the deliverysystem 10 tracking the one or more attributes of the group of guests 80within the dining environment 50. For example, the group of guests 80may include a first guest 80 a, a second guest 80 b, and a third guest80 c. Each guest of the group may take a different route (e.g., path)within the dining environment 50, but the group may end up at a finallocation. The final location may be the location where the group ofguests may have their dining experience, or the order delivery location.

With reference to FIG. 7 , the third guest 80 c may take route 150 andhead directly to a first table 58 a. The first table 58 a may be thefinal location for the group. For example, the group may visit differentlocations within the dining environment 50, but the group may reconveneat the first table 58 a. As such, the third guest 80 c may wait at thefirst table 58 a for the other members of the group. The third guest 80c may spend a period of time at the first table 58 a greater than athreshold period of time. As such, the delivery system 10 may associatethe third guest 80 c with the first table 58 a. The delivery system 10may further associate the other members of the group (e.g., second guest80 b, third guest 80 c) with the first table 58 a.

The first guest 80 a may take route 152 and stop at a second table 58 b,start a conversation with one or more guests at the second table 58 b,then walk to the first table 58 a. The first guest 80 a may stay at thesecond table 58 b for a period of time that is less than the thresholdperiod of time, and thus, the first guest 80 a is not associated withthe second table 58 b. In an instance, the first guest 80 a may stay atthe second table 58 b for the period of time that is longer than thethreshold period of time. In such cases, the first guest 80 a mayinitially be associated with the second table 58 b, or other factors maybe considered so that the first guest 80 a is not associated with thesecond table 58 b even though the period of time is longer than thethreshold period of time. For example, the delivery system 10 maydetermine that the first guest 80 a should not be associated with thesecond table 58 b based on the period of time spent at the second table58 b, the movement or gestures made by the first guest 80 a at thesecond table 58 b, and/or other guests at the second table 58 b. Forexample, the first guest 80 a may kneel or stand by the second table 58b. The delivery system 10 may identify the body position of the firstguest 80 a as an indication that the second table 58 b may be atemporary location. The delivery system 10 may also factor in the groupformation and the third guest 80 c waiting at the first table 58 a(e.g., if the first guest 80 a is part of the group, and another memberof the group has established their location, the threshold time and/orother factors utilized in order for the first guest 80 a to establishthe association with a different location may increase). Thus, thedelivery system 10 may not form an association between the first guest80 a and the second table 58 b. Indeed, the first guest 80 a may leavethe second table 58 b and go to the first table 58 a. Should the firstguest 80 a be associated with the second table 58 b, the delivery system10 may determine that a break event has occurred and may then break theassociation between the first guest 80 a and the second table 58 b, andthen form another association between the first guest 80 a and the firsttable 58 a upon receipt of sufficient information.

The second guest 80 b may take route 154 within the dining environment50. The second guest 80 b may visit a temporary location (e.g., station60) before arriving at the first table 58 a. For example, the secondguest 80 b may visit the restroom, the hand sanitizing station, thedrink station, or the condiment station. The delivery system 10 mayidentify the station 60 as a temporary location rather than the orderdelivery location. Even if the second guest 80 b spends a period of timeat the station 60 greater than the threshold period of time, thedelivery system 10 may not associate the second guest 80 b with thestation 60. In this way, the delivery system 10 may distinguish betweenthe areas of the dining environment 50.

While tracking the one or more attributes of the guests 80 a, 80 b, 80 cwithin the dining environment 50, the delivery system 10 may alsopassively update the map. For example, the group of guests may combinethe first table 58 a with a third table 58 c. The delivery system 10 mayidentify the first table 58 a and the third table 58 c moving locationsand/or changing configurations. The delivery system 10 may furtheridentify the third guest 80 c sitting at the first table 58 a and thefirst guest 80 a and the second guest 80 b sitting at the third table 58c. The delivery system 10 may update the map and provide an indicationof the update to the vendor 52. The delivery system 10 may also provideinstructions to provide the order based on the updated map. In this way,the delivery system 10 may provide accurate instructions for orderdelivery.

FIG. 8 is an example method 170 for associating the one or moreattributes of the guest 80 with a location and determining if a breakevent occurred. As described herein, the guest 80 may take any route ofa variety of different routes available within the dining environment 50to arrive at the location. For example, the guest 80 may stop by a firstlocation to chat with friends, visit a condiment station, stop by therestroom, or the like. The guest 80 may stop at the first location(e.g., table, seat) and stay at the first location for a period of time.If the period of time is greater than a threshold period of time, thenthe delivery system 10 may associate the one or more attributes of theguest 80 with the first location. However, the guest 80 may decide tomove to a second location (e.g., final location; order deliverylocation). As such, the delivery system 10 may determine if a breakevent occurred and if a new association may be formed with the secondlocation. In this way, the delivery system 10 may determine the locationof the guest 80 to accurately and efficiently facilitate delivery of theorder.

At block 172, the delivery system 10 may identify one or more attributesof a guest 80 present at a location for a period of time. For example,the delivery system 10 may identify the guest 80 at a first table 58 aand may monitor the image data over time. In an instance, the guest 80may enter the guest area 56 and sit at the first table 58 a. The guest80 may talk to another guest, browse their phone, observe the diningenvironment 50, or the like at the first table 58 a. The delivery system10 may identify these behaviors and correlate these behaviors to anintent of the guest to establish the first table 58 a as their locationfor order delivery.

At block 174, the delivery system 10 may determine if the period of timespent at the location is greater than a threshold period of time. Thethreshold period of time may be an amount of time determined by thedelivery system 10, via machine learning or artificial intelligence,that may be indicative of the guest 80 claiming the first table 58 a orotherwise intending to establish the first table 58 a as their locationfor order delivery. The threshold period of time may vary based on anyof a variety of factors, such as movement or gestures made by the guestat the table, any items placed on the table, any other guests at thetable, a respective time spent at other locations, or the like. Tomonitor the image data, the delivery system 10 may start a clock ortimer to monitor the period of time the guest 80 may spend at the firsttable 58 a. Additionally or alternatively, the delivery system 10 mayidentify time stamps within the image data. As such, the delivery system10 may track the period of time the one or more attributes of the guest80 may be present at the first table 58 a and compare the period of timeto the threshold period of time.

If the time spent at the location is not greater than the thresholdperiod of time, then the delivery system 10 may not associate the guest80 with the location. After the period of time, the guest 80 may leavethe first table 58 a and walk to a second table 58 b. The guest 80 maystand, sit, kneel, lean on, or walk by the second table 58 b. Forexample, the guest 80 may talk to another guest 80 located at the secondtable 58 b and join the other guest 80 at the second table 58 b. Thedelivery system 10 may not associate the guest 80 with the first table58 and monitor the image data over time to determine if the guest 80 maybe associated with the second table 58 b. As such, the example method170 may return to block 172 and identify the one or more attributes ofthe guest 80 present at the second table 58 for a period of time.

If the period of time spent at the location is greater than thethreshold period time, then at block 176, the delivery system 10 mayassociate the one or more attributes of the guest 80 with the location.For example, the guest 80 may sit at the first table 58 a to wait fortheir order 82. In other words, the guest 80 may claim the table fortheir dining experience. The period of time the guest 80 spends at thefirst table waiting for their order 82 may be greater than the thresholdperiod of time. As such, the delivery system 10 may associate the one ormore attributes of the guest 80 with the first table 58 a.

At block 178, the delivery system 10 may determine if a break eventoccurred. In an instance, the guest 80 may leave the first table 58 aand the delivery system 10 may determine if the break event occurred.That is, the delivery system 10 may determine if the association may bebroken. To identify a break event, the delivery system 10 may take intoaccount a variety of factors, such as a respective time at the table, arespective time away from the table, a type of the additional location,movement or gestures made by the guest at the table and/or away from thetable, personal possessions 90 e left at the table, other guests at thetable, or the like.

If the break event did not occur, then the delivery system 10 may returnto block 176 and maintain the association of the one or more attributesof the guest 80 with the first table 58 a. For example, the guest 80 mayleave one or more personal possessions, such as a water bottle or ajacket, at the first table 58 a and visit the station 60. The deliverysystem 10 may determine that the personal possessions and the station 60may be indicative of the guest 80 coming back to the first table 58 a.That is, the station 60 may be a temporary location. As such, thedelivery system 10 may determine that the break event may not occur. Inanother example, the guest 80 may go to the second table 58 b and talkto other guests 80. The guest 80 may stand by the second table 58 b. Thedelivery system 10 may identify the standing position of the guest 80 asan intention to return to the first table 58 a. As such, the deliverysystem 10 may not determine leaving the first table 58 a as the breakevent. The method 170 may return to block 176 and the delivery system 10may maintain the association of the one or more attributes of the guest80 with the first table 58 a.

If the break event occurred, then at block 180, the delivery system 10may break the association between the one or more attributes of theguest 80 with the location. For example, the guest 80 may sit at thesecond table 58 b and begin a conversation with other guests at thesecond table 58. The guest 80 may stay at the second table 58 b for aperiod of time longer than the threshold period of time. The deliverysystem 10 may identify staying at the second table 58 b as the breakevent. In another example, the delivery system 10 may recognize that theguest 80 may have completed the dining experience. For example, theguest 80 may pack their personal possessions 90 e and prepare to leavethe dining environment 50. The guest 80 may also clean the first table58 a by taking a plate, a tray, a box, or the like to a trash station.As such, the delivery system 10 may understand that the guest 80 may beplanning to leave the dining environment 50. The delivery system 10 mayidentify the guest 80 leaving the first table 58 a as the break eventand break the association between the one or more attributes of theguest 80 and the location. Accordingly, the delivery system 10 mayclassify certain actions and/or combinations of actions taken by theguest 80 via computer vision techniques, which may enable the deliverysystem 10 to accurately determine that it is appropriate to createand/or to end the association with the location for order delivery.

The method 170 may be stored on one or more tangible, non-transitory,machine-readable media and/or may be performed by the processor or theprocessing circuitry of the control system described above or on anothersuitable controller. The steps of the method 170 may be performed in theorder disclosed above or in any other suitable order. Furthermore,certain steps of the method may be omitted and/or other blocks may beadded to the method 170.

As used herein, ‘machine learning’ and/or ‘computer vision’ may refer toalgorithms and statistical models that computer systems use to perform aspecific task with or without using explicit instructions. For example,a machine learning process may generate a mathematical model based on asample of clean data, known as “training data,” in order to makepredictions or decisions without being explicitly programmed to performthe task. The delivery system 10 may generate (e.g., train and/orupdate, such as passively update) the model based on image datacollected over time. In this way, the model may improve over time basedon new image data collected over time. For example, the model mayreceive image data in order to provide outputs related to creatingand/or to breaking the association, and the image data may then also beused to update and refine the model.

It is well understood that the use of PII should follow privacy policiesand practices that are generally recognized as meeting or exceedingindustry or governmental requirements for maintaining the privacy ofusers. In particular, personally identifiable information data should bemanaged and handled so as to minimize unintentional or unauthorizedaccess or use, and the nature of authorized use should be clearlyindicated to users.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention. It should be appreciated thatany features shown and described with reference to FIGS. 1-5 may becombined in any suitable manner.

The techniques presented and claimed herein are referenced and appliedto material objects and concrete examples of a practical nature thatdemonstrably improve the present technical field and, as such, are notabstract, intangible or purely theoretical. Further, if any claimsappended to the end of this specification contain one or more elementsdesignated as “means for (perform)ing (a function)” or “step for(perform)ing (a function) . . . ”, it is intended that such elements areto be interpreted under 35 U.S.C. 112(f). However, for any claimscontaining elements designated in any other manner, it is intended thatsuch elements are not to be interpreted under 35 U.S.C. 112(f).

1. A delivery system, comprising: one or more processors; memory storinginstructions executable by the one or more processors to cause the oneor more processors to: identify one or more attributes of a user in oneor more images captured by one or more cameras; associate the one ormore attributes of the user with an order placed by the user; track theone or more attributes of the user in the one or more images over timeto identify movement of the user within an environment; in response tothe one or more attributes of the user in the one or more imagesremaining at a location for more than a threshold time, create anassociation between the one or more attributes of the user and thelocation; and provide an instruction to deliver items in the order tothe location.
 2. The delivery system of claim 1, wherein the locationcomprises a table or a seat in a dining environment.
 3. The deliverysystem of claim 1, wherein the instructions are executable by the one ormore processors to cause the one or more processors to analyze the oneor more images to determine that the order was placed by the user. 4.The delivery system of claim 3, wherein the instructions are executableby the one or more processors to cause the one or more processors toanalyze the one or more images to identify an order number on a displayscreen in the environment to thereby determine that the order was placedby the user.
 5. The delivery system of claim 1, wherein the instructionsare executable by the one or more processors to cause the one or moreprocessors to: identify the one or more attributes of the user at anadditional location for more than the threshold time; and in response tothe one or more attributes of the user being at the additional locationfor more than the threshold time, break the association between the oneor more attributes of the user and the location.
 6. The delivery systemof claim 1, wherein the instructions are executable by the one or moreprocessors to cause the one or more processors to: identify the one ormore attributes of the user at an additional location; reference a mapof the environment to determine that the additional location is atemporary location; and in response to the additional location being thetemporary location, maintain the association between the one or moreattributes of the user and the location.
 7. The delivery system of claim1, wherein the one or more attributes are anonymous attributes that donot provide an identity of the user.
 8. The delivery system of claim 7,wherein the one or more attributes comprise a hair color, a hairstyle, aclothing item shape, a clothing item color, an accessory, an objectcarried by the user, a gait, a head shape, or any combination thereof.9. The delivery system of claim 1, wherein the instructions areexecutable by the one or more processors to cause the one or moreprocessors to: identify one or more respective attributes of anadditional user in the one or more images captured by the one or morecameras; associate the one or more respective attributes of theadditional user with an additional order placed by the additional user;track the one or more respective attributes of the additional user inthe one or more images over time to identify respective movement of theadditional user within the environment; in response to the one or morerespective attributes of the additional user in the one or more imagesremaining at the location for more than the threshold time, create anadditional association between the one or more respective attributes ofthe additional user and the location; and provide the instruction todeliver the items in the order and the respective items in theadditional order to the location.
 10. The delivery system of claim 9,wherein the instructions are executable by the one or more processors tocause the one or more processors to: form the user and the additionaluser into a group in response to the association and the additionalassociation existing together over a time period; receive an indicationthat the items in the order and the respective items in the additionalorder are ready for delivery to the location at a delivery time; and inresponse to analysis of the one or more attributes of the user and theone or more additional attributes of the additional user in the one ormore images indicating that at least one of the user and the additionaluser is present at the location at the delivery time, provide theinstruction to deliver the items in the order and the respective itemsin the additional order to the location.
 11. The delivery system ofclaim 1, wherein the instructions are executable by the one or moreprocessors to cause the one or more processors to: provide theinstruction that indicates the location for visualization by a server;and withhold the one or more attributes from the server.
 12. A method ofoperating a delivery system, the method comprising: identifying, usingone or more processors, one or more attributes of a user in one or moreimages captured by one or more cameras; associating, using the one ormore processors, the one or more attributes of the user with an orderplaced by the user; tracking, using the one or more processors, the oneor more attributes of the user in the one or more images over time toidentify movement of the user within an environment; creating, using theone or more processors, an association between the one or moreattributes of the user and a location in response to the one or moreattributes of the user in the one or more images remaining at thelocation for more than a threshold time; and providing, using the one ormore processors, an instruction to deliver items in the order to thelocation.
 13. The method of claim 12, comprising analyzing, using theone or more processors, the one or more images to determine that theorder was placed by the user.
 14. The method of claim 12, comprising:identifying, using the one or more processors, the one or moreattributes of the user at an additional location for more than thethreshold time; and breaking, using the one or more processors, theassociation between the one or more attributes of the user and thelocation in response to the one or more attributes of the user being atthe additional location for more than the threshold time.
 15. The methodof claim 12, comprising: identifying, using the one or more processors,the one or more attributes of the user at an additional location;referencing, using the one or more processors, a map of the environmentto determine that the additional location is a temporary location; andmaintaining, using the one or more processors, the association betweenthe one or more attributes of the user and the location in response tothe additional location being the temporary location.
 16. The method ofclaim 12, comprising blocking, using the one or more processors, outputof the one or more images and the one or more attributes of the user inthe environment, such that the one or more images and the one or moreattributes are not disclosed to personnel in the environment.
 17. Adelivery system, comprising: one or more processors; memory storinginstructions executable by the one or more processors to cause the oneor more processors to: identify one or more attributes of a user in oneor more images captured by one or more cameras; associate the one ormore attributes of the user with an order placed by the user; track theone or more attributes of the user in the one or more images over timeto identify movement of the user within an environment; in response tothe one or more attributes of the user in the one or more imagesremaining at a location for more than a threshold time, create anassociation between the one or more attributes of the user and thelocation; and provide an output that indicates the location tofacilitate delivery of items in the order to the location withoutdisplaying the one or more images to personnel associated with theenvironment.
 18. The delivery system of claim 17, wherein the outputcomprises a map of the environment that indicates the location.
 19. Thedelivery system of claim 18, wherein the instructions are executable bythe one or more processors to cause the one or more processors to updatethe map to represent respective current positions of structures in theenvironment based on the image data.
 20. The delivery system of claim17, wherein: the instructions identify the one or more attributes of theuser at an additional location; reference a map of the environment todetermine that the additional location is a temporary location; and inresponse to the additional location being the temporary location,maintain the association between the one or more attributes of the userand the location.